White matter alterations in affective and non-affective early psychosis: A diffusion MRI study.

BACKGROUND: The early years after the onset of psychotic disorders, known as "early psychosis" (EP) are critical to determining the path of psychosis trajectory. We used a Diffusion Magnetic Resonance Imaging (DMRI) connectometry approach to assess the microstructural changes of white matter (WM) associated with EP. METHODS: We used the Human Connectome Project in Early Psychosis (HCP-EP) dataset to collect DMRI data from patients with EP. The imaging data were processed in the Montreal Neuroimaging Initiative space and transformed into quantitative anisotropy (QA). The QA value was translated into the WM connectivity of each tract and used in the subsequent analysis. RESULTS: 121 patients with EP (94 non-affective/27 affective) and 56 healthy controls were recruited. EP was associated with increased QA in the body and tapetum of corpus callosum (CC) and decreased QA in the bilateral cerebellum, and middle cerebellar peduncle. Compared to non-affective psychosis, affective psychosis showed increased QA in the bilateral cerebellum and vermis and decreased QA in the forceps minor, body of CC, right cingulum, and bilateral inferior fronto-occipital fasciculus. Furthermore, QA changes in several WM tracts were correlated with positive and negative symptom scale scores. LIMITATIONS: DMRI intrinsic limitations, limited sample size, and neurobiological effects of psychotropic treatment. CONCLUSIONS: EP is associated with alterations in WM connectivity primarily in the CC and cerebellar regions. Also, affective and non-affective psychosis have distinct alterations in WM connectivity. These results can be used for the early diagnosis and differentiation of psychotic disorders.

Effects of non-pharmacological interventions on gait and balance of persons with Multiple Sclerosis: A narrative review.

BACKGROUND: Multiple Sclerosis (MS) is among the most common reasons for disability in young adults. Mobility impairment, primarily related to gait and balance, is ranked as the preeminent concern among persons with MS (PwMS). Gait and balance dysfunction can directly affect the quality of life and activities of daily life in PwMS, hence the importance of effective treatment strategies. Previous studies have demonstrated the positive effect of various non-pharmacological rehabilitation methods, including physiotherapy and electrical stimulation, on gait and mobility in PwMS. Non-pharmacological methods can be tailored to the individual needs and abilities of each patient, allowing healthcare providers to create personalized training programs. Furthermore, these methods typically result in minimal or no side effects. PURPOSE: This review provides a comprehensive overview of an array of non-pharmacological treatment approaches aimed at enhancing ambulatory performance in PwMS. METHODS: We performed a narrative review of the original papers available in PubMed, investigating the effects of different nonmedical approaches on the gait and balance performance of the PwMS. Reviewed treatment approaches include "exercise, physical rehabilitation, dual-task (DT) rehabilitation, robot-assisted rehabilitation, virtual reality-assisted rehabilitation, game training, electrical stimulation devices, auditory stimulation, visual feedback, and shoe insoles". RESULTS AND CONCLUSIONS: Eighty articles were meticulously reviewed. Our study highlights the positive effects of non-pharmacological interventions on patients' quality of life, reducing disability, fatigue, and muscle spasticity. While some methods, including exercise and physiotherapy, showed substantial promise, further research is needed to evaluate whether visual biofeedback and auditory stimulation are preferable over conventional approaches. Additionally, approaches such as functional electrical stimulation, non-invasive brain stimulation, and shoe insoles demonstrate substantial short-term benefits, prompting further investigation into their long-term effects. Non-pharmacological interventions can serve as a valuable complement to medication-based approaches.

Classification of suicidality by training supervised machine learning models with brain MRI findings: A systematic review.

BACKGROUND: Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortality rate. This review aimed to investigate the feasibility of suicidality identification by applying supervised Machine Learning (ML) methods to Magnetic Resonance Imaging (MRI) data. METHODS: We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed for the quality assessment. RESULTS: 23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models varied among the reviewed studies, with the highest reported values of accuracies and Area Under the Curve (AUC) ranging from 51.7 % to 100 % and 0.52 to 1, respectively. Over half of the studies that reported accuracy (12/21) or AUC (13/16) achieved values of ≥0.8. Our comparative analysis indicated that deep learning exhibited the highest predictive performance compared to other ML models. The most commonly identified discriminative imaging features were resting-state functional connectivity and grey matter volume within prefrontal-limbic structures. LIMITATIONS: Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development. CONCLUSIONS: Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necessary to uncover the true potential of different ML models in this field.

Efficacy and safety of adjunctive therapy with fingolimod in patients with schizophrenia: A randomized, double-blind, placebo-controlled clinical trial.

OBJECTIVES: Studies have suggested that fingolimod, a sphingosine-1-phosphate receptor modulator, exerts neuroprotective and anti-inflammatory effects. Although fingolimod is approved for the treatment of relapsing-remitting multiple sclerosis, limited studies have investigated its effects in patients with schizophrenia. This study investigated the efficacy and safety of fingolimod adjuvant to risperidone in schizophrenia treatment. METHODS: This eight-week, randomized, double-blinded, placebo-controlled trial included 80 (clinical trials registry code: IRCT20090117001556N137) patients with chronic schizophrenia. Participants were assigned to two equal arms and received risperidone plus either fingolimod (0.5 mg/day) or a matched placebo. The positive and negative symptom scale (PANSS) was used to measure and compare the effectiveness of treatment strategies at baseline and weeks 2, 4, 6, and 8. Treatment side effects were also compared. RESULTS: Seventy participants completed the trial (35 in each arm). The baseline characteristics of the groups were comparable (P-value > 0.05). There were significant time-treatment interaction effects on negative symptoms (P-value = 0.003), general symptoms (P-value = 0.037), and the PANSS total score (P-value = 0.035), suggesting greater improvement in symptoms following the fingolimod adjuvant therapy. In contrast, the longitudinal changes in positive and depressive symptoms were similar between the groups (P-values > 0.05). Regarding the safety of treatments, there were no differences in extrapyramidal symptoms [assessed by the extrapyramidal symptom rating scale (ESRS)] or frequency of other complications between the fingolimod and the placebo groups (P-values > 0.05). CONCLUSIONS: This study indicated that fingolimod is a safe and effective adjuvant agent for schizophrenia treatment. However, further clinical trials are required to suggest extensive clinical application.

Adjuvant palmitoylethanolamide therapy with risperidone improves negative symptoms in patients with schizophrenia: A randomized, double-blinded, placebo-controlled trial.

BACKGROUND: Primary negative symptoms of schizophrenia are usually resistant to monotherapy with antipsychotics. The present study sought to assess the efficacy and tolerability of Palmitoylethanolamide (PEA) adjunctive therapy in treatment of negative symptoms in patients with stable schizophrenia. METHODS: This 8-week (trial timepoints: baseline, week 4, week 8), double-blind, placebo-controlled clinical trial randomized patients with schizophrenia in a 1:1 ratio to compare the efficacy and safety of 600 mg twice a day of PEA and matched placebo alongside a stable dose of risperidone. Outcome measures were the positive and the negative syndrome scale (PANSS), the extrapyramidal symptom rating scale (ESRS), and the Hamilton depression rating scale (HDRS). The primary outcome was change in the negative subscale score during the trial period between the groups. Safety of interventions were controlled and addressed during the trial. RESULTS: A total of 50 participants completed the trial (25 in each group). Baseline characteristics of the groups were comparable (p>0.05). There was significant effect from time-treatment interaction on negative symptoms (p = 0.012) suggesting greater symptom improvement in the PEA group. In contrast, the longitudinal changes in positive symptoms and depressive symptoms were similar between groups (p values>0.05). Safety assessments showed no significant difference regarding extrapyramidal symptoms, measured by ESRS, and also frequency of other complications between PEA and placebo groups (p values>0.05). CONCLUSIONS: Adjunctive therapy with PEA and risperidone alleviates schizophrenia-related primary negative symptoms in a safe manner.